Semiconductor processing is notoriously challenging. It is one of the most intricate feats of modern engineering due to the ...

image: This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms.

In a first, Australian scientists turned to quantum machine learning to model semiconductor design, outperforming classical ...

For over a decade, researchers have considered boson sampling—a quantum computing protocol involving light particles—as a key ...

The Cornell-led project's paper, "Correlator Convolutional Neural Networks as an Interpretable Architecture for Image-like Quantum Matter Data," published June 23 in Nature Communications.The lead ...

Approaches to quantum-enhanced machine learning. Quantum search In the mid-1990s, computer scientist Lov Grover showed that a future quantum computer can search an unsorted database – such as ...

The appeal of quantum machine learning lies in its potential to tackle problems that classical ML ... Honda Research Institute uses such data compression for image analysis using quantum computers.

One strategy involves using a quantum computer as a hardware add-on to a conventional machine learning system. In this scenario, data – images of cats and dogs, for example – are sent to a quantum ...

Looking forward, the team aims to scale these models for larger, more complex datasets and explore further optimizations in quantum state encoding and quantum machine learning architecture design.